Do these 3 things before closing this tab:
1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsiTechGuides is reader-supported. When you buy through links on our site, we may earn an affiliate commission. As an Amazon Associate I earn from qualifying purchases. Learn more
An event stream is a durable, continuing sequence of records that producers write and consumers can retrieve and process. Object storage can hold a downstream copy of that data, but a broker acknowledging a record, a consumer receiving it, and an object or table becoming visible are different milestones. “Produce latency” is meaningful only when its start and end points are specified.
How an event stream moves data
A stream is not simply a one-time message sent across a network. It is a sequence of events that can be stored durably and read by consumers later, often by more than one application. Apache Kafka’s official introduction describes event streaming as capturing events from sources such as applications and databases, storing them for retrieval, processing them in real time or retrospectively, and routing them to destinations.
In a typical Kafka flow, a producer publishes records to a topic. A topic is divided into partitions, which let the system organize records and serve reads and writes in parallel. Kafka brokers store the records. Consumers subscribe to topics and read records independently of the producer; a consumer can process them soon after they arrive or, where retained data permits, later. This separation lets producers publish without having to know which downstream applications will use the records.
The path can be represented as:
event source → producer → Kafka topic partition on broker(s) → consumer(s)The stream is the ordered flow of records within its topic partitions, together with the storage and retrieval mechanisms that make those records available. It is not, by itself, a promise that every record has already reached a downstream database, file, or analytics table as soon as a producer receives an acknowledgment.
#1 Best Overall
- Read Before You Buy — No Video Output: These adapters support charging and USB 2.0 data transfer, but cannot transmit video signals. Except for standard USB webcams (which use USB data only), they are not compatible with HDMI/DisplayPort cables, video-capable USB-C hubs, or docking stations with video output.
- Convert USB-A Ports to USB-C: Designed to connect USB-C earphones, cables, flash drives, card readers, and other USB-C accessories to standard USB-A ports. Plug-and-play with no drivers or software required.
- Aluminum Alloy Housing: Built with a sturdy aluminum alloy shell that aids in heat dissipation and protects against daily wear and scratches. Designed to maintain a stable and secure connection.
- Compact & Travel-Friendly: The ultra-compact design allows the adapter to stay plugged into your device without blocking adjacent ports or adding bulk, reducing wear and tear on your original USB ports.
- 12-Month Warranty: Backed by a 12-month manufacturer warranty for peace of mind. Designed to meet strict quality control standards for reliable everyday performance.
Where object storage fits
Object storage—such as Amazon S3—holds data as objects in buckets and is commonly used for durable, economical storage of files and data-lake inputs. It is often downstream of the streaming system rather than the place where a Kafka producer receives its immediate write acknowledgment. A delivery or processing path reads records from Kafka, prepares them if necessary, and writes objects to storage.
AWS’s documented data-lake pattern describes records being delivered from Kafka to S3 either directly or through Amazon Data Firehose. In the Firehose path, records may be transformed or normalized, buffered according to an interval or target size, converted to Parquet, and written using partitioned S3 prefixes. These steps can improve file organization and suit batch-oriented analytics, but they add stages between publishing a record and seeing a usable object in storage.
producer → Kafka topic → delivery or processing path → object(s) in S3 → table/catalog or query layerSome deployments write directly from a Kafka-connected sink; others use a managed delivery service or a stream-processing application. The exact components and visibility behavior depend on the chosen implementation and its configuration. A Kafka acknowledgment does not, on its own, establish that a downstream sink has finished writing an object or that a query engine can see a table update.
Recommended Free Tools
It also matters what “available in the lake” means. An object may exist in a bucket before a catalog or table metadata layer recognizes it, and a file may be present before a query can include it. For a reader, the meaningful endpoint might therefore be “object written,” “partition discovered,” or “table query returns the record.” Those are distinct completion criteria.
Append-only files are not automatically a maintained table
The basic Kafka-to-object-storage pattern commonly lands new records as additional data. That suits append-oriented logs and event histories. It does not automatically mean that a later update or delete in the source will rewrite an older object or change the corresponding row in a lake table.
Rank #2
- 5-in-1 USB-C Hub: Experience comprehensive connectivity featuring a Power Delivery input, two USB-A 2.0 ports, a USB-A 3.0 port, and an HDMI port. (Note: The USB-C power delivery input port is only for connecting an external wall charger to power your laptop and cannot power peripheral devices.)
- 90W Pass-Through Charging: Achieve optimal charging with 90W pass-through power to your laptop, supported by a total input of 100W, with the hub reserving 10W for operational efficiency. (Note: Wall charger not included.)
- Quick Data Transfers: Accelerate your productivity with rapid data transfers using a high-speed 5Gbps USB 3.0 port and two 480Mbps USB 2.0 ports.
- 4K HDMI Display: Enhance your visual experience with a hub capable of delivering 4K resolution at 30Hz in both mirror and extend modes. Please note that this hub is compatible with MacBook (macOS 12 and newer), Windows 10 and 11, ChromeOS, and laptops equipped with DP Alt Mode and Power Delivery. Note: This device is not compatible with Linux.
- What You Get: Anker USB-C Hub (5-in-1, 4K HDMI), welcome guide, 18-month warranty, and our friendly customer service.
AWS notes that reflecting updates and deletes can require additional processing, such as AWS Glue and a streaming Hudi connector that identifies existing records and updates them. The general lesson is that object storage is a destination, not a complete change-management model. A maintained dataset needs a strategy for interpreting record keys, updates, deletes, and table metadata in addition to writing files.
When choosing or assessing a lake path, establish whether it is meant to preserve every event as an append-only history or to represent the latest state of each entity. For the latter, verify the connector or processing logic, the table format and metadata behavior, and how downstream readers see changes. Do not infer update handling merely from the fact that records are arriving in a bucket.
What “produce latency” measures
A useful precise definition is producer acknowledgment latency: elapsed time from a producer’s send or request boundary to receipt of the broker acknowledgment configured for that write. The exact start timestamp depends on the client metric or instrumentation: a timer might begin when the application calls a send method, when the client places a request on the network, or at another defined point. Any reported figure should name that boundary.
This is not automatically end-to-end latency. An end-to-end measurement must specify its own start and finish, for example:
- Event-to-consumer latency: event creation at the source to receipt or processing by a specified consumer.
- Producer-to-object latency: producer send to the object being written and visible under a stated criterion.
- Producer-to-table latency: producer send to a query or table view returning the record.
These intervals can include different work. Producer acknowledgment time can involve client-side buffering, network transit, broker handling, and the configured acknowledgment behavior. A producer-to-object measurement may additionally include consumer or connector scheduling, transformations, buffering, file conversion, object upload, and any table or catalog updates relevant to the chosen endpoint.
Rank #3
- Sleek 7-in-1 USB-C Hub: Features an HDMI port, two USB-A 3.0 ports, and a USB-C data port, each providing 5Gbps transfer speeds. It also includes a USB-C PD input port for charging up to 100W and dual SD and TF card slots, all in a compact design.
- Flawless 4K@60Hz Video with HDMI: Delivers exceptional clarity and smoothness with its 4K@60Hz HDMI port, making it ideal for high-definition presentations and entertainment. (Note: Only the HDMI port supports video projection; the USB-C port is for data transfer only.)
- Double Up on Efficiency: The two USB-A 3.0 ports and a USB-C port support a fast 5Gbps data rate, significantly boosting your transfer speeds and improving productivity.
- Fast and Reliable 85W Charging: Offers high-capacity, speedy charging for laptops up to 85W, so you spend less time tethered to an outlet and more time being productive.
- What You Get: Anker USB-C Hub (7-in-1), welcome guide, 18-month warranty, and our friendly customer service.
IBM Cloud Event Streams documentation explicitly distinguishes producer-view sending latency from producer-to-consumer end-to-end latency, noting that the former is lower in the documented service context. That distinction is important: a producer may receive an acknowledgment while downstream consumption or object delivery is still in progress. Conversely, a measurement that starts before a client has flushed a buffered record may include client-side waiting that a broker request metric does not.
Event time, ingestion time, and processing time are different clocks
Latency discussions often mix timestamps that describe when something happened with durations that measure how long an operation took. Kafka Streams documentation defines three useful concepts:
- Event time: when the event occurred at its source.
- Ingestion time: when the record is stored in a Kafka topic partition.
- Processing time: when an application handles the record.
These timestamps answer different questions. Event time can reveal how late data arrived relative to the real-world event; ingestion time marks entry into the broker stream; processing time reflects downstream application work. None of them alone is necessarily the duration from a producer send call to its acknowledgment. For example, the difference between event time and ingestion time may include source-side delay before publication, while acknowledgment latency measures a producer operation after it has begun.
The Kafka Streams documentation cited for these definitions is for Kafka 2.8. The terminology remains useful for distinguishing clock meanings, but that older page should not be treated as a complete description of every current Kafka configuration option.
Acknowledgments trade latency against durability guarantees
An acknowledgment is a signal that a particular write condition has been met; what it guarantees depends on the acknowledgment policy and the service’s implementation. A faster acknowledgment is not automatically equivalent to the strongest durability condition, and the strongest condition is not automatically the right choice for every workload.
Free tools Windows power users keep installed
One-click scans. No signup required.
Rank #4
- Dual Converters, Infinite Potential：Includes 2× USB C male to USB A female adapters and 2× USB A male to USB C female adapters. Perfect for a wide range of uses—tablets with Bluetooth keyboards, expand USB ports on macbook, and more. Two different converters for all your daily needs
- Next-Level 10Gbps & 3A Charging: No more slow 480Mbps, this usb to usb c adapter has a transfer speed of up to 10Gbps, allowing you to do more transferring in less time. This usb adapter fits both USB A and USB C charger, supporting up to 3A fast charging
- Upgraded Exquisite Craftsmanship: With an aluminum alloy housing and metal connector, the usbc to usb adapter is extremely durable and sturdy. Rigorously tested to withstand more than 10,000 times of plugging and unplugging, ensuring long-lasting performance
- Broad Compatible: The usb c to usb adapter widely supports all USB C/ USB A devices like laptops, tablets, cellphones, car chargers, and phone chargers. Such as compatible with MacBook Pro/Air 2023/2022, Thunderbolt 4/3 Devices,Apple MagSafe Watch 9/8/7/SE/Ultra, iPad Pro 2022/2021, Samsung Galaxy S23/S20/S10, and iPhone 17/16/15 Pro. Plug and play
- Please Note: To reach 10Gbps speed, keep the cable under 3.3 ft. For USB A Male to USB C adapters, try flipping the USB C connector. USB C Male to USB A adapters support bidirectional 10Gbps transfer within 3.3 ft
IBM’s Event Streams documentation describes the tradeoff in its service context. Under less stringent acknowledgment behavior, an acknowledgment can occur before all replicas have confirmed the write. With acks=all, its documentation says acknowledgment follows the leader write and writes by the in-sync replicas. That is a more specific confirmation boundary, and it can affect the time the producer waits.
Choose the policy against the cost of losing or delaying a record, the service’s documented replication behavior, and the application’s recovery design. A telemetry stream that can tolerate occasional loss may make a different tradeoff from a financial or operational event log. Do not assume that the same setting means precisely the same thing across all managed services without checking their documentation.
There is also a throughput consequence to how the producer uses acknowledgments. IBM notes that if a producer waits for each acknowledgment before issuing another send, it can prevent batching and constrain publish rate relative to network round-trip time. Producers commonly buffer and batch records to use requests more efficiently. A full buffer can delay a send call while the client waits to flush data, so an application-observed send duration can include waiting that is not broker processing time.
Object visibility adds buffering and delivery stages
In a buffered delivery path, records may be held until an interval elapses or a target size is reached, then packaged and delivered. This can produce fewer, larger files and support conversion to an analytics-friendly format such as Parquet. It also means object arrival may intentionally lag behind the broker acknowledgment. If a system transforms records, writes partitioned prefixes, or updates table metadata, each operation can affect the endpoint a user cares about.
There is a practical tradeoff: shorter buffering can make data available sooner but may produce more small objects; longer buffering can improve file sizing but increase the wait before delivery. The actual behavior depends on the service’s options, workload, and downstream processing. A claim such as “records are in S3” should therefore say whether it refers to producer confirmation, completed object delivery, or queryable table state.
Best Value
- 5-in-1 Connectivity: Equipped with a 4K HDMI port, a 5 Gbps USB-C data port, two 5 Gbps USB-A ports, and a USB C 100W PD-IN port. Note: The USB C 100W PD-IN port supports only charging and does not support data transfer devices such as headphones or speakers.
- Powerful Pass-Through Charging: Supports up to 85W pass-through charging so you can power up your laptop while you use the hub. Note: Pass-through charging requires a charger (not included). Note: To achieve full power for iPad, we recommend using a 45W wall charger.
- Transfer Files in Seconds: Move files to and from your laptop at speeds of up to 5 Gbps via the USB-C and USB-A data ports. Note: The USB C 5Gbps Data port does not support video output.
- HD Display: Connect to the HDMI port to stream or mirror content to an external monitor in resolutions of up to 4K@30Hz. Note: The USB-C ports do not support video output.
- What You Get: Anker 332 USB-C Hub (5-in-1), welcome guide, our worry-free 18-month warranty, and friendly customer service.
When investigating a delay, locate the stage where time is accumulating rather than attributing everything to Kafka produce latency. Compare the producer acknowledgment timestamp, consumer receipt or connector progress, delivery-service buffering, object-write completion, and any table/catalog visibility event. A slow final endpoint can coexist with prompt broker acknowledgments.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A proposal-specific example: Kafka KIP-1163
Apache Kafka KIP-1163, titled “Diskless Core,” describes a proposed object-storage append path. Its page gives estimates and targets for that proposal, not a benchmark for Kafka generally or a service-level promise. The figures below concern the proposed path, which includes buffering, remote-storage upload, and batch-coordination commit.
| Stage or metric | KIP-1163 proposal figure | How to interpret it |
|---|---|---|
| Buffering | Up to 250 ms or 4 MiB, configurable | A proposal-specific buffering limit or size threshold, not a general Kafka setting. |
| Remote-storage upload | Estimated P50 about 100 ms; P99 about 200–400 ms | Proposal estimates for this stage of the proposed path. |
| Batch-coordination commit | Estimated P50 about 10 ms; P99 about 20–50 ms | Proposal estimates for commit coordination, not end-to-end lake visibility. |
| Produce request latency target | P50 about 500 ms; P99 about 1–2 seconds | A target for the proposal’s Produce request path, not a universal producer-latency expectation. |
P50 is the median: half of measured observations are at or below it, and half are at or above it. P99 is the 99th percentile, a tail-latency measure. The proposal’s target is a Produce request boundary for its diskless append design; it should not be read as the time until a consumer receives a record or until an object or table is queryable. Since KIP-1163 is a proposal whose details can evolve, consult its current page before using the values to characterize that design.
Quick wins for a faster PC:
Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →These figures are useful as an example of how a latency claim should identify its system, path, stage, percentile, and status as an estimate or target. They do not establish a typical latency for existing Kafka clusters, managed Kafka offerings, or arbitrary object stores. A deployment-specific measurement is needed for those.
How to measure the latency that matters to your application
Before comparing graphs or setting an alert, define the operational question. If users need to know how quickly the broker accepts writes, measure producer acknowledgment latency. If an application needs to react to new events, measure to consumer receipt or processing. If analysts need a lake dataset updated, measure to the object or table visibility condition they actually use.
- Name the start event. Specify whether timing begins at source event creation, the application’s send call, or the producer request boundary.
- Name the finish event. Choose broker acknowledgment, consumer receipt, object-write completion, or queryable table visibility; do not use “available” without defining it.
- Record the acknowledgment policy. Include the configured setting and the service’s documented meaning, including which replica writes are required.
- Report percentiles, not just an average. Include a median such as P50 and a tail measure such as P99 when they are relevant, and say which requests or records were measured.
- Describe workload and configuration. Note relevant batching and buffering behavior, record sizes, transformations, delivery intervals, and the system or service path. These affect what a measured interval includes.
- Instrument intermediate milestones for diagnosis. Separate producer wait, consumer or connector delay, object delivery, and table/catalog visibility where possible. The longest stage is then easier to identify.
Use the same boundaries and conditions when comparing two designs or releases. A lower broker acknowledgment number does not prove that one design delivers a queryable lake table faster; it may simply stop the clock earlier. Likewise, compare object delivery only when both measurements define the same visibility endpoint and include the relevant buffering and transformation stages.
Common interpretation mistakes
- Calling acknowledgment latency “end-to-end latency.” It ends at the configured broker response, not necessarily at consumption or storage delivery.
- Counting the same interval under different names. A client send timer may include buffer waiting, whereas a broker request timer can begin later. Identify the instrumentation boundary.
- Treating object arrival as table readiness. A file can be written before metadata or processing makes it visible to the intended query.
- Assuming a bucket automatically reflects source mutations. Append delivery and update/delete handling are separate behaviors; verify the lake’s processing and table strategy.
- Applying proposal figures to a different system. KIP-1163’s estimates and target describe its proposed diskless path, not a universal performance range.
- Comparing percentiles without matching conditions. P50 and P99 are meaningful only with a defined request population, path, configuration, and observation period.
For background on Kafka producers, storage, tiered storage, pipelines, monitoring, and stream processing, O’Reilly’s publisher page lists Kafka: The Definitive Guide, 2nd Edition (2021). It is a background reference; current implementation details should be checked against the relevant product documentation.
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
